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公开(公告)号：US20250124386A1
公开(公告)日：2025-04-17
申请号：US18686485
申请日：2022-08-30
Applicant: NEC Corporation
Inventor： Ryota HIGA , Shinji NAKADAI , Katsuhide FUJITA , Toki TAKAHASHI
IPC: G06Q10/0637
Abstract: A learning device acquires history information about a proposal which has been implemented in a negotiation, acquires an evaluation value for a proposal from a negotiation counterpart, and learns a determination method for an own proposal to the negotiation counterpart, based on the history information and the evaluation value.
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公开(公告)号：US20230385892A1
公开(公告)日：2023-11-30
申请号：US18032404
申请日：2020-10-23
Applicant: NEC Corporation
Inventor： Ryota HIGA
IPC: G06Q30/0601
CPC classification number: G06Q30/0611
Abstract: An execution planning means 81 calculates, with an offer from another agent as a constraint condition, a first value which is a value of an optimal execution plan up to achievement of an objective planned based on a state transition by an action taken according to a policy of an own agent. A determination means 82 determines, with the first value as an argument, whether or not a value calculated by a utility function, which is a function defining a utility of an execution plan of the own agent when the offer from the other agent is accepted, is greater than a predetermined threshold value. The determination means 82 determines to accept the offer from the other agent when the value is greater than the threshold value, and determines to reject the offer from the other agent when the value is equal to or less than the threshold value.
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公开(公告)号：US20210264307A1
公开(公告)日：2021-08-26
申请号：US17252902
申请日：2018-06-26
Applicant: NEC Corporation
Inventor： Ryota HIGA
Abstract: A model setting unit 81 sets, as a problem setting to be targeted in reinforcement learning, a model in which a policy for determining an action to be taken in an environmental state is associated with a Boltzmann distribution representing a probability distribution of a prescribed state, and a reward function for determining a reward obtainable from an environmental state and an action selected in the state is associated with a physical equation representing a physical quantity corresponding to an energy. A parameter estimation unit 82 estimates parameters of the physical equation by performing the reinforcement learning using training data including the state based on the set model. A difference detection unit 83 detects differences between previously estimated parameters of the physical equation and newly estimated parameters of the physical equation.
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公开(公告)号：US20240124251A1
公开(公告)日：2024-04-18
申请号：US18276822
申请日：2021-02-24
Applicant: NEC Corporation
Inventor： Ryota HIGA
CPC classification number: B65G63/004 , B65G43/08 , B65G2201/0235 , B65G2203/0233
Abstract: The loading container information input means 71 accepts input of information on the target container which is the container to be loaded next. The inquiring means 72 transmits current loading state and information on the target container to a container loading planning device, which replies to a loading position of the container in response to an inquiry, to inquire about the loading position of the target container. The evaluation means 73 outputs an evaluation value for loading the target container at the loading position received from the container loading planning device. The output means 74 outputs the evaluation value in time series order corresponding to the loading of the target container.
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公开(公告)号：US20210398019A1
公开(公告)日：2021-12-23
申请号：US17296798
申请日：2018-12-07
Applicant: NEC Corporation
Inventor： Ryota HIGA
Abstract: A learning device 80 is a learning device for learning a model applied to a device that performs processing using a specific model, includes an input unit 81 and an imitation learning unit 82. The input unit 81 receives input of a functional form of a reward. The imitation learning unit 82 learns a policy by imitation learning based on training data. The imitation learning unit 82 learns a reward function depending on the input functional form of the reward by the imitation learning.
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Patent Agency Ranking
公开(公告)号：US20210318921A1
公开(公告)日：2021-10-14
申请号：US17274276
申请日：2019-09-06
Applicant: NEC Corporation
Inventor： Ryota HIGA
IPC: G06F11/00
Abstract: A model generation apparatus (2000) acquires component failure data in which a usage status is associated with a failure record of a component. The model generation apparatus (2000) generates, for each of a plurality of component groups, a prediction model for predicting the number of failures of each component included in the component group by using the component failure data relating to the component belonging to the component group. The prediction model computes a prediction value of the total number of failures of the components belonging to a corresponding component group from the usage status, and computes a prediction value of the number of failures of each component belonging to the component group from the computed prediction value of the total number of failures.
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公开(公告)号：US20240127115A1
公开(公告)日：2024-04-18
申请号：US18276781
申请日：2021-02-24
Applicant: NEC Corporation
Inventor： Ryota HIGA
IPC: G06N20/00
Abstract: The loading container information input means 71 accepts input of information on the target container. The Inquiring means 72 transmits current loading state and information on the target container to the container loading planning device 80 to inquire about the loading position of the target container. The evaluation means 73 outputs an evaluation value for loading the target container at the received loading position. The output means 74 outputs data including the loading state and information of the target container, the loading position of the target container, and the evaluation value as training data. The learning means 91 learns the model by machine learning using the output training data. The loading position determination means 81 determines the loading position of the target container using the learned model.
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公开(公告)号：US20230394970A1
公开(公告)日：2023-12-07
申请号：US18032873
申请日：2020-10-28
Applicant: NEC Corporation
Inventor： Ryota HIGA
IPC: G08G1/16
CPC classification number: G08G1/16
Abstract: A learning means 81 learns a plan evaluation function that evaluates an internal value in an own agent when a mission including an action is planned so as to maximize a value of a mission evaluation function that calculates a value of the action of the own agent in a certain state or an expected value of a cumulative sum of the values. An evaluation means 82 evaluates, using a utility function that defines a difference between the internal values calculated using the plan evaluation function, a utility of the mission when a target resource, which is a resource to be a target candidate for negotiation, is transferred to another agent or when the target resource is transferred from the other agent.
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公开(公告)号：US20220012540A1
公开(公告)日：2022-01-13
申请号：US17296796
申请日：2018-12-07
Applicant: NEC Corporation
Inventor： Ryota HIGA
Abstract: The learning device 80 includes an input unit 81 and an imitation learning unit 82. The input unit 81 receives input of a type of a reward function. The imitation learning unit 82 learns a policy by imitation learning based on training data. The imitation learning unit 82 learns the reward function according to the type by the imitation learning, based on a form defined depending on the type.
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公开(公告)号：US20240242124A1
公开(公告)日：2024-07-18
申请号：US18563046
申请日：2021-05-28
Applicant: NEC Corporation
Inventor： Ryota HIGA , Shinji NAKADAI
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: The input means 81 accepts input of a reward function that defines cumulative reward by a reward term based on a high-level indicator representing a production indicator. The learning means 82 learns a value function for deriving optimal policy for an agent using training data and the reward function. The output means 83 outputs the learned value function.
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